mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2026-10-25 22:28:17 +08:00
Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.
This commit is contained in:
Rob Armstrong
@@ -42,8 +42,8 @@
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#include <cusparse.h>
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// Utilities and system includes
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#include <helper_cuda.h> // helper function CUDA error checking and initialization
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#include <helper_functions.h> // helper for shared functions common to CUDA Samples
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#include <helper_cuda.h> // helper function CUDA error checking and initialization
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#include <helper_functions.h> // helper for shared functions common to CUDA Samples
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const char *sSDKname = "conjugateGradientCudaGraphs";
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@@ -52,291 +52,235 @@ const char *sSDKname = "conjugateGradientCudaGraphs";
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#endif
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/* genTridiag: generate a random tridiagonal symmetric matrix */
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void genTridiag(int *I, int *J, float *val, int N, int nz) {
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I[0] = 0, J[0] = 0, J[1] = 1;
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val[0] = (float)rand() / RAND_MAX + 10.0f;
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val[1] = (float)rand() / RAND_MAX;
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int start;
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void genTridiag(int *I, int *J, float *val, int N, int nz)
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{
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I[0] = 0, J[0] = 0, J[1] = 1;
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val[0] = (float)rand() / RAND_MAX + 10.0f;
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val[1] = (float)rand() / RAND_MAX;
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int start;
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for (int i = 1; i < N; i++) {
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if (i > 1) {
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I[i] = I[i - 1] + 3;
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} else {
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I[1] = 2;
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for (int i = 1; i < N; i++) {
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if (i > 1) {
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I[i] = I[i - 1] + 3;
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}
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else {
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I[1] = 2;
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}
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start = (i - 1) * 3 + 2;
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J[start] = i - 1;
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J[start + 1] = i;
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if (i < N - 1) {
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J[start + 2] = i + 1;
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}
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val[start] = val[start - 1];
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val[start + 1] = (float)rand() / RAND_MAX + 10.0f;
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if (i < N - 1) {
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val[start + 2] = (float)rand() / RAND_MAX;
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}
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}
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start = (i - 1) * 3 + 2;
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J[start] = i - 1;
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J[start + 1] = i;
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I[N] = nz;
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}
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if (i < N - 1) {
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J[start + 2] = i + 1;
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__global__ void initVectors(float *rhs, float *x, int N)
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{
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size_t gid = blockIdx.x * blockDim.x + threadIdx.x;
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for (size_t i = gid; i < N; i += gridDim.x * blockDim.x) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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}
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__global__ void r1_div_x(float *r1, float *r0, float *b)
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{
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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b[0] = r1[0] / r0[0];
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}
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}
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__global__ void a_minus(float *a, float *na)
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{
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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na[0] = -(a[0]);
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}
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}
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int main(int argc, char **argv)
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{
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int N = 0, nz = 0, *I = NULL, *J = NULL;
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float *val = NULL;
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const float tol = 1e-5f;
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const int max_iter = 10000;
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float *x;
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float *rhs;
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float r1;
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int *d_col, *d_row;
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float *d_val, *d_x;
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float *d_r, *d_p, *d_Ax;
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int k;
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float alpha, beta, alpham1;
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cudaStream_t stream1, streamForGraph;
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// This will pick the best possible CUDA capable device
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cudaDeviceProp deviceProp;
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int devID = findCudaDevice(argc, (const char **)argv);
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if (devID < 0) {
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printf("exiting...\n");
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exit(EXIT_SUCCESS);
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}
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val[start] = val[start - 1];
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val[start + 1] = (float)rand() / RAND_MAX + 10.0f;
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
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if (i < N - 1) {
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val[start + 2] = (float)rand() / RAND_MAX;
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// Statistics about the GPU device
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printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
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deviceProp.multiProcessorCount,
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deviceProp.major,
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deviceProp.minor);
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/* Generate a random tridiagonal symmetric matrix in CSR format */
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N = 1048576;
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nz = (N - 2) * 3 + 4;
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checkCudaErrors(cudaMallocHost(&I, sizeof(int) * (N + 1)));
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checkCudaErrors(cudaMallocHost(&J, sizeof(int) * nz));
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checkCudaErrors(cudaMallocHost(&val, sizeof(float) * nz));
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genTridiag(I, J, val, N, nz);
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checkCudaErrors(cudaMallocHost(&x, sizeof(float) * N));
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rhs = (float *)malloc(sizeof(float) * N);
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for (int i = 0; i < N; i++) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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}
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I[N] = nz;
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}
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/* Get handle to the CUBLAS context */
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cublasHandle_t cublasHandle = 0;
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cublasStatus_t cublasStatus;
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cublasStatus = cublasCreate(&cublasHandle);
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__global__ void initVectors(float *rhs, float *x, int N) {
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size_t gid = blockIdx.x * blockDim.x + threadIdx.x;
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checkCudaErrors(cublasStatus);
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for (size_t i = gid; i < N; i += gridDim.x * blockDim.x) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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}
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/* Get handle to the CUSPARSE context */
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cusparseHandle_t cusparseHandle = 0;
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cusparseStatus_t cusparseStatus;
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cusparseStatus = cusparseCreate(&cusparseHandle);
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__global__ void r1_div_x(float *r1, float *r0, float *b) {
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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b[0] = r1[0] / r0[0];
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}
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}
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checkCudaErrors(cusparseStatus);
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__global__ void a_minus(float *a, float *na) {
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int gid = blockIdx.x * blockDim.x + threadIdx.x;
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if (gid == 0) {
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na[0] = -(a[0]);
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}
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}
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checkCudaErrors(cudaStreamCreate(&stream1));
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int main(int argc, char **argv) {
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int N = 0, nz = 0, *I = NULL, *J = NULL;
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float *val = NULL;
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const float tol = 1e-5f;
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const int max_iter = 10000;
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float *x;
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float *rhs;
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float r1;
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checkCudaErrors(cudaMalloc((void **)&d_col, nz * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_row, (N + 1) * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_val, nz * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_x, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_r, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_p, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_Ax, N * sizeof(float)));
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int *d_col, *d_row;
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float *d_val, *d_x;
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float *d_r, *d_p, *d_Ax;
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int k;
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float alpha, beta, alpham1;
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float *d_r1, *d_r0, *d_dot, *d_a, *d_na, *d_b;
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checkCudaErrors(cudaMalloc((void **)&d_r1, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_r0, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_dot, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_a, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_na, sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(float)));
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cudaStream_t stream1, streamForGraph;
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/* Wrap raw data into cuSPARSE generic API objects */
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cusparseSpMatDescr_t matA = NULL;
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checkCudaErrors(cusparseCreateCsr(&matA,
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N,
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N,
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nz,
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d_row,
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d_col,
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d_val,
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CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_BASE_ZERO,
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CUDA_R_32F));
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cusparseDnVecDescr_t vecx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecx, N, d_x, CUDA_R_32F));
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cusparseDnVecDescr_t vecp = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F));
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cusparseDnVecDescr_t vecAx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecAx, N, d_Ax, CUDA_R_32F));
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// This will pick the best possible CUDA capable device
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cudaDeviceProp deviceProp;
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int devID = findCudaDevice(argc, (const char **)argv);
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/* Allocate workspace for cuSPARSE */
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size_t bufferSize = 0;
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checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle,
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CUSPARSE_OPERATION_NON_TRANSPOSE,
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&alpha,
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matA,
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vecx,
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&beta,
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vecAx,
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CUDA_R_32F,
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CUSPARSE_SPMV_ALG_DEFAULT,
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&bufferSize));
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void *buffer = NULL;
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checkCudaErrors(cudaMalloc(&buffer, bufferSize));
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if (devID < 0) {
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printf("exiting...\n");
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exit(EXIT_SUCCESS);
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}
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cusparseMatDescr_t descr = 0;
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checkCudaErrors(cusparseCreateMatDescr(&descr));
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
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checkCudaErrors(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
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checkCudaErrors(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
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// Statistics about the GPU device
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printf(
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"> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
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deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
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int numBlocks = 0, blockSize = 0;
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checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(&numBlocks, &blockSize, initVectors));
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/* Generate a random tridiagonal symmetric matrix in CSR format */
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N = 1048576;
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nz = (N - 2) * 3 + 4;
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checkCudaErrors(cudaMallocHost(&I, sizeof(int) * (N + 1)));
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checkCudaErrors(cudaMallocHost(&J, sizeof(int) * nz));
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checkCudaErrors(cudaMallocHost(&val, sizeof(float) * nz));
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genTridiag(I, J, val, N, nz);
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checkCudaErrors(cudaMemcpyAsync(d_col, J, nz * sizeof(int), cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_row, I, (N + 1) * sizeof(int), cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMemcpyAsync(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice, stream1));
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checkCudaErrors(cudaMallocHost(&x, sizeof(float) * N));
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rhs = (float *)malloc(sizeof(float) * N);
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initVectors<<<numBlocks, blockSize, 0, stream1>>>(d_r, d_x, N);
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for (int i = 0; i < N; i++) {
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rhs[i] = 1.0;
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x[i] = 0.0;
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}
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alpha = 1.0;
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alpham1 = -1.0;
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beta = 0.0;
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/* Get handle to the CUBLAS context */
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cublasHandle_t cublasHandle = 0;
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cublasStatus_t cublasStatus;
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cublasStatus = cublasCreate(&cublasHandle);
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checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
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checkCudaErrors(cusparseSpMV(cusparseHandle,
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CUSPARSE_OPERATION_NON_TRANSPOSE,
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&alpha,
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matA,
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vecx,
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&beta,
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vecAx,
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CUDA_R_32F,
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CUSPARSE_SPMV_ALG_DEFAULT,
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buffer));
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checkCudaErrors(cublasStatus);
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checkCudaErrors(cublasSetStream(cublasHandle, stream1));
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checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1));
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/* Get handle to the CUSPARSE context */
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cusparseHandle_t cusparseHandle = 0;
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cusparseStatus_t cusparseStatus;
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cusparseStatus = cusparseCreate(&cusparseHandle);
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checkCudaErrors(cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE));
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checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
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checkCudaErrors(cusparseStatus);
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k = 1;
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// First Iteration when k=1 starts
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checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1));
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checkCudaErrors(cusparseSpMV(cusparseHandle,
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CUSPARSE_OPERATION_NON_TRANSPOSE,
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&alpha,
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matA,
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vecp,
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&beta,
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vecAx,
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CUDA_R_32F,
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CUSPARSE_SPMV_ALG_DEFAULT,
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buffer));
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checkCudaErrors(cudaStreamCreate(&stream1));
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checkCudaErrors(cudaMalloc((void **)&d_col, nz * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_row, (N + 1) * sizeof(int)));
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checkCudaErrors(cudaMalloc((void **)&d_val, nz * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_x, N * sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_r, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_p, N * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_Ax, N * sizeof(float)));
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float *d_r1, *d_r0, *d_dot, *d_a, *d_na, *d_b;
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checkCudaErrors(cudaMalloc((void **)&d_r1, sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_r0, sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_dot, sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_a, sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_na, sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(float)));
|
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|
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/* Wrap raw data into cuSPARSE generic API objects */
|
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cusparseSpMatDescr_t matA = NULL;
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checkCudaErrors(cusparseCreateCsr(&matA, N, N, nz, d_row, d_col, d_val,
|
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F));
|
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cusparseDnVecDescr_t vecx = NULL;
|
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checkCudaErrors(cusparseCreateDnVec(&vecx, N, d_x, CUDA_R_32F));
|
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cusparseDnVecDescr_t vecp = NULL;
|
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checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F));
|
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cusparseDnVecDescr_t vecAx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecAx, N, d_Ax, CUDA_R_32F));
|
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|
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/* Allocate workspace for cuSPARSE */
|
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size_t bufferSize = 0;
|
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checkCudaErrors(cusparseSpMV_bufferSize(
|
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cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecx,
|
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&beta, vecAx, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT, &bufferSize));
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void *buffer = NULL;
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checkCudaErrors(cudaMalloc(&buffer, bufferSize));
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|
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cusparseMatDescr_t descr = 0;
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checkCudaErrors(cusparseCreateMatDescr(&descr));
|
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|
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checkCudaErrors(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
|
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checkCudaErrors(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
|
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|
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int numBlocks = 0, blockSize = 0;
|
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checkCudaErrors(
|
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cudaOccupancyMaxPotentialBlockSize(&numBlocks, &blockSize, initVectors));
|
||||
|
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checkCudaErrors(cudaMemcpyAsync(d_col, J, nz * sizeof(int),
|
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cudaMemcpyHostToDevice, stream1));
|
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checkCudaErrors(cudaMemcpyAsync(d_row, I, (N + 1) * sizeof(int),
|
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cudaMemcpyHostToDevice, stream1));
|
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checkCudaErrors(cudaMemcpyAsync(d_val, val, nz * sizeof(float),
|
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cudaMemcpyHostToDevice, stream1));
|
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|
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initVectors<<<numBlocks, blockSize, 0, stream1>>>(d_r, d_x, N);
|
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|
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alpha = 1.0;
|
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alpham1 = -1.0;
|
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beta = 0.0;
|
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|
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checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
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checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
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&alpha, matA, vecx, &beta, vecAx, CUDA_R_32F,
|
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CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
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checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
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checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1));
|
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|
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checkCudaErrors(
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE));
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
|
||||
k = 1;
|
||||
// First Iteration when k=1 starts
|
||||
checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha, matA, vecp, &beta, vecAx, CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
|
||||
|
||||
a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToDevice, stream1));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(&r1, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToHost, stream1));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream1));
|
||||
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
|
||||
// First Iteration when k=1 ends
|
||||
k++;
|
||||
|
||||
#if WITH_GRAPH
|
||||
cudaGraph_t initGraph;
|
||||
checkCudaErrors(cudaStreamCreate(&streamForGraph));
|
||||
checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
||||
checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
||||
checkCudaErrors(cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
|
||||
checkCudaErrors(
|
||||
cusparseSetPointerMode(cusparseHandle, CUSPARSE_POINTER_MODE_HOST));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha, matA, vecp, &beta, vecAx, CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cudaMemsetAsync(d_dot, 0, sizeof(float), stream1));
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
|
||||
|
||||
a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToDevice, stream1));
|
||||
checkCudaErrors(cudaMemsetAsync(d_r1, 0, sizeof(float), stream1));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToHost, stream1));
|
||||
|
||||
checkCudaErrors(cudaStreamEndCapture(stream1, &initGraph));
|
||||
cudaGraphExec_t graphExec;
|
||||
checkCudaErrors(cudaGraphInstantiate(&graphExec, initGraph, NULL, NULL, 0));
|
||||
#endif
|
||||
|
||||
checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
||||
checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
||||
|
||||
while (r1 > tol * tol && k <= max_iter) {
|
||||
#if WITH_GRAPH
|
||||
checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph));
|
||||
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
||||
#else
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
|
||||
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
|
||||
|
||||
checkCudaErrors(cusparseSpMV(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
|
||||
@@ -344,81 +288,173 @@ int main(int argc, char **argv) {
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
|
||||
|
||||
a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToDevice, stream1));
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float), cudaMemcpyDeviceToDevice, stream1));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float),
|
||||
cudaMemcpyDeviceToHost, stream1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(&r1, d_r1, sizeof(float), cudaMemcpyDeviceToHost, stream1));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream1));
|
||||
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
|
||||
// First Iteration when k=1 ends
|
||||
k++;
|
||||
|
||||
#if WITH_GRAPH
|
||||
cudaGraph_t initGraph;
|
||||
checkCudaErrors(cudaStreamCreate(&streamForGraph));
|
||||
checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
||||
checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
||||
checkCudaErrors(cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
|
||||
checkCudaErrors(cusparseSetPointerMode(cusparseHandle, CUSPARSE_POINTER_MODE_HOST));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle,
|
||||
CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha,
|
||||
matA,
|
||||
vecp,
|
||||
&beta,
|
||||
vecAx,
|
||||
CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT,
|
||||
buffer));
|
||||
|
||||
checkCudaErrors(cudaMemsetAsync(d_dot, 0, sizeof(float), stream1));
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
|
||||
|
||||
a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float), cudaMemcpyDeviceToDevice, stream1));
|
||||
checkCudaErrors(cudaMemsetAsync(d_r1, 0, sizeof(float), stream1));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float), cudaMemcpyDeviceToHost, stream1));
|
||||
|
||||
checkCudaErrors(cudaStreamEndCapture(stream1, &initGraph));
|
||||
cudaGraphExec_t graphExec;
|
||||
checkCudaErrors(cudaGraphInstantiate(&graphExec, initGraph, NULL, NULL, 0));
|
||||
#endif
|
||||
|
||||
checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
||||
checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
||||
|
||||
while (r1 > tol * tol && k <= max_iter) {
|
||||
#if WITH_GRAPH
|
||||
checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph));
|
||||
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
||||
#else
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_r0, d_b);
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSscal(cublasHandle, N, d_b, d_p, 1));
|
||||
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_HOST);
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1));
|
||||
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle,
|
||||
CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha,
|
||||
matA,
|
||||
vecp,
|
||||
&beta,
|
||||
vecAx,
|
||||
CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT,
|
||||
buffer));
|
||||
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
r1_div_x<<<1, 1, 0, stream1>>>(d_r1, d_dot, d_a);
|
||||
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_a, d_p, 1, d_x, 1));
|
||||
|
||||
a_minus<<<1, 1, 0, stream1>>>(d_a, d_na);
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, d_na, d_Ax, 1, d_r, 1));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_r0, d_r1, sizeof(float), cudaMemcpyDeviceToDevice, stream1));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, d_r1));
|
||||
checkCudaErrors(cudaMemcpyAsync((float *)&r1, d_r1, sizeof(float), cudaMemcpyDeviceToHost, stream1));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream1));
|
||||
#endif
|
||||
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
|
||||
k++;
|
||||
}
|
||||
|
||||
#if WITH_GRAPH
|
||||
checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost, streamForGraph));
|
||||
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
||||
#else
|
||||
checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost, stream1));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream1));
|
||||
#endif
|
||||
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
|
||||
k++;
|
||||
}
|
||||
|
||||
#if WITH_GRAPH
|
||||
checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float),
|
||||
cudaMemcpyDeviceToHost, streamForGraph));
|
||||
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
||||
#else
|
||||
checkCudaErrors(cudaMemcpyAsync(x, d_x, N * sizeof(float),
|
||||
cudaMemcpyDeviceToHost, stream1));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream1));
|
||||
#endif
|
||||
float rsum, diff, err = 0.0;
|
||||
|
||||
float rsum, diff, err = 0.0;
|
||||
for (int i = 0; i < N; i++) {
|
||||
rsum = 0.0;
|
||||
|
||||
for (int i = 0; i < N; i++) {
|
||||
rsum = 0.0;
|
||||
for (int j = I[i]; j < I[i + 1]; j++) {
|
||||
rsum += val[j] * x[J[j]];
|
||||
}
|
||||
|
||||
for (int j = I[i]; j < I[i + 1]; j++) {
|
||||
rsum += val[j] * x[J[j]];
|
||||
diff = fabs(rsum - rhs[i]);
|
||||
|
||||
if (diff > err) {
|
||||
err = diff;
|
||||
}
|
||||
}
|
||||
|
||||
diff = fabs(rsum - rhs[i]);
|
||||
|
||||
if (diff > err) {
|
||||
err = diff;
|
||||
}
|
||||
}
|
||||
|
||||
#if WITH_GRAPH
|
||||
checkCudaErrors(cudaGraphExecDestroy(graphExec));
|
||||
checkCudaErrors(cudaGraphDestroy(initGraph));
|
||||
checkCudaErrors(cudaStreamDestroy(streamForGraph));
|
||||
checkCudaErrors(cudaGraphExecDestroy(graphExec));
|
||||
checkCudaErrors(cudaGraphDestroy(initGraph));
|
||||
checkCudaErrors(cudaStreamDestroy(streamForGraph));
|
||||
#endif
|
||||
checkCudaErrors(cudaStreamDestroy(stream1));
|
||||
cusparseDestroy(cusparseHandle);
|
||||
cublasDestroy(cublasHandle);
|
||||
checkCudaErrors(cudaStreamDestroy(stream1));
|
||||
cusparseDestroy(cusparseHandle);
|
||||
cublasDestroy(cublasHandle);
|
||||
|
||||
if (matA) {
|
||||
checkCudaErrors(cusparseDestroySpMat(matA));
|
||||
}
|
||||
if (vecx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecx));
|
||||
}
|
||||
if (vecAx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecAx));
|
||||
}
|
||||
if (vecp) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecp));
|
||||
}
|
||||
if (matA) {
|
||||
checkCudaErrors(cusparseDestroySpMat(matA));
|
||||
}
|
||||
if (vecx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecx));
|
||||
}
|
||||
if (vecAx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecAx));
|
||||
}
|
||||
if (vecp) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecp));
|
||||
}
|
||||
|
||||
checkCudaErrors(cudaFreeHost(I));
|
||||
checkCudaErrors(cudaFreeHost(J));
|
||||
checkCudaErrors(cudaFreeHost(val));
|
||||
checkCudaErrors(cudaFreeHost(x));
|
||||
free(rhs);
|
||||
checkCudaErrors(cudaFree(d_col));
|
||||
checkCudaErrors(cudaFree(d_row));
|
||||
checkCudaErrors(cudaFree(d_val));
|
||||
checkCudaErrors(cudaFree(d_x));
|
||||
checkCudaErrors(cudaFree(d_r));
|
||||
checkCudaErrors(cudaFree(d_p));
|
||||
checkCudaErrors(cudaFree(d_Ax));
|
||||
checkCudaErrors(cudaFreeHost(I));
|
||||
checkCudaErrors(cudaFreeHost(J));
|
||||
checkCudaErrors(cudaFreeHost(val));
|
||||
checkCudaErrors(cudaFreeHost(x));
|
||||
free(rhs);
|
||||
checkCudaErrors(cudaFree(d_col));
|
||||
checkCudaErrors(cudaFree(d_row));
|
||||
checkCudaErrors(cudaFree(d_val));
|
||||
checkCudaErrors(cudaFree(d_x));
|
||||
checkCudaErrors(cudaFree(d_r));
|
||||
checkCudaErrors(cudaFree(d_p));
|
||||
checkCudaErrors(cudaFree(d_Ax));
|
||||
|
||||
printf("Test Summary: Error amount = %f\n", err);
|
||||
exit((k <= max_iter) ? 0 : 1);
|
||||
printf("Test Summary: Error amount = %f\n", err);
|
||||
exit((k <= max_iter) ? 0 : 1);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user